A novel approach for protein structure prediction based on an estimation of distribution algorithm
- 82 Downloads
Protein structure prediction is one of the major challenges in structural biology and has wide potential applications in biotechnology. However, the problem is faced with a difficult optimization requirement with particularly complex energy landscapes. The current article aims to present a novel approach namely AHEDA as an evolutionary-based solution to overcome the problem. AHEDA uses the hydrophobic-polar model to develop a robust and efficient evolutionary-based algorithm for protein structure prediction. The method utilizes an integrated estimation of distribution algorithm that attempts to optimize the search process and prevent the destruction of structural blocks. It also uses a stochastic local search to improve its accuracy. Based on a comprehensive comparison with other existing methods on 24 widely used benchmarks, AHEDA was shown to generate highly accurate predictions compared to the other similar methods.
KeywordsEstimation of distribution algorithm (EDA) Protein structure prediction (PSP) HP model Protein folding Stochastic local search (SLS)
Compliance with ethical standards
Conflict of interest
Authors declare that they have no conflict of interest.
This article does not contain any studies with human participants performed by any of the authors.
- Bazzoli A, Tettamanzi AG (2004) A memetic algorithm for protein structure prediction in a 3D-lattice HP model. Workshops on applications of evolutionary computation. Springer, Berlin, pp 1–10Google Scholar
- Chen W, Ding H, Feng P, Lin H, Chou KC (2016) iACP: a sequence-based tool for identifying anticancer peptides. Oncotarget 7(13):16895Google Scholar
- Do DD (2017) A novel and efficient ant colony optimization algorithm for protein 3D structure prediction. VNU-UET technical reportGoogle Scholar
- Kanj F, Mansour N, Khachfe H, Abu-Khzam F (2009) Protein structure prediction in the 3D HP model. In IEEE/ACS international conference on computer systems and applications, 2009. AICCSA 2009. IEEE. pp 732–736Google Scholar
- Mansour N, Kanj F, Khachfe H (2010) Evolutionary algorithm for protein structure prediction. In: 2010 sixth international conference on natural computation (ICNC), vol 8. IEEE, pp 3974–3977Google Scholar
- Patton AL, Punch III WF, Goodman ED (1995) A standard GA approach to native protein conformation prediction. In: ICGA, pp 574–581Google Scholar
- Santos J, Diéguez M (2011) Differential evolution for protein structure prediction using the HP model. International work-conference on the interplay between natural and artificial computation. Springer, Berlin, pp 323–333Google Scholar
- Storm CN, Lyngsø RB (1999) Protein folding in the 2D HP model. Tech rep, Technical Report RS-99-16 BRICS, University of Aarhus, DenmarkGoogle Scholar